Research

Thanks to technological advances, we can now profile gene expression across thousands or millions of individual cells in parallel. This new type of data has led to the intriguing discovery that individual cell profiles can reflect the imprint of time or dynamic processes.

With machine learning systems now being used to determine everything from stock prices to medical diagnoses, it’s never been more important to look at how they arrive at decisions.  

A new approach out of MIT demonstrates that the main culprit is not just the algorithms themselves, but how the data itself is collected.

The U.S. National Academies of Sciences, Engineering, and Medicine and the U.S. Office of Research Integrity have taken a special interest in recent years to examine potential training gaps among researchers and lab managers, and how mounting pressure to secure grants, publish papers, and earn promotions takes a toll on their outlook of the workplace.

Researchers compiled and analyzed the first-ever comprehensive dataset of RfC conversations, captured over an eight-year period, and conducted interviews with editors who frequently close RfCs, to understand why they don’t find a resolution.